SemiPL: A Semi-supervised Method for Event Sound Source Localization
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916229976948736 |
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| author | Li, Yue Yin, Baiqiao Liu, Jinfu Wen, Jiajun Lin, Jiaying Liu, Mengyuan |
| author_facet | Li, Yue Yin, Baiqiao Liu, Jinfu Wen, Jiajun Lin, Jiaying Liu, Mengyuan |
| contents | In recent years, Event Sound Source Localization has been widely applied in various fields. Recent works typically relying on the contrastive learning framework show impressive performance. However, all work is based on large relatively simple datasets. It's also crucial to understand and analyze human behaviors (actions and interactions of people), voices, and sounds in chaotic events in many applications, e.g., crowd management, and emergency response services. In this paper, we apply the existing model to a more complex dataset, explore the influence of parameters on the model, and propose a semi-supervised improvement method SemiPL. With the increase in data quantity and the influence of label quality, self-supervised learning will be an unstoppable trend. The experiment shows that the parameter adjustment will positively affect the existing model. In particular, SSPL achieved an improvement of 12.2% cIoU and 0.56% AUC in Chaotic World compared to the results provided. The code is available at: https://github.com/ly245422/SSPL |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SemiPL: A Semi-supervised Method for Event Sound Source Localization Li, Yue Yin, Baiqiao Liu, Jinfu Wen, Jiajun Lin, Jiaying Liu, Mengyuan Computer Vision and Pattern Recognition Multimedia Sound Audio and Speech Processing In recent years, Event Sound Source Localization has been widely applied in various fields. Recent works typically relying on the contrastive learning framework show impressive performance. However, all work is based on large relatively simple datasets. It's also crucial to understand and analyze human behaviors (actions and interactions of people), voices, and sounds in chaotic events in many applications, e.g., crowd management, and emergency response services. In this paper, we apply the existing model to a more complex dataset, explore the influence of parameters on the model, and propose a semi-supervised improvement method SemiPL. With the increase in data quantity and the influence of label quality, self-supervised learning will be an unstoppable trend. The experiment shows that the parameter adjustment will positively affect the existing model. In particular, SSPL achieved an improvement of 12.2% cIoU and 0.56% AUC in Chaotic World compared to the results provided. The code is available at: https://github.com/ly245422/SSPL |
| title | SemiPL: A Semi-supervised Method for Event Sound Source Localization |
| topic | Computer Vision and Pattern Recognition Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.19615 |